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Monthly Reference Frequency Distributions of Hourly Relative Wind Speed Data from Burundian Stations

DOI: 10.4236/epe.2025.1710017, PP. 309-323

Keywords: ANOVA (One-Way), F-Test (One-Sided), Hourly Relative Wind Speed Data, Monthly Reference Frequency Distributions

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Abstract:

In previous research works on one hand, terrain, meteorological and wind speed data from various Burundian sites have been processed in order to bring efficient tools for the planning and installation of wind energy conversion systems (WECSs) at those sites. On another hand, sunshine duration and global solar radiation data, respectively from different Burundian sites, have been used to implement monthly reference distributions of data at those sites for the next two dimensionless random variables: the daily relative sunshine duration and the daily clearness index. Those distributions were deemed to be important inputs in projects for setting solar energy conversion systems (SECSs) at the relevant sites. The present study comes out as a continuation of the afore-said works and it intends to further contribute to bringing useful help in projects for setting WECSs at selected Burundian localities. Using a 4-years period’s hourly wind speed, ν values from four Burundian stations as primary data, the specific objective of the study is three-fold. Firstly, to set up for each station frequency distributions, means and variances of the 12 monthly samples and the 48 sub-samples of hourly relative wind speed, v r (another dimensionless random variable) data extracted from the primary wind speed values. Secondly, to use a suitable statistical test in order to ascertain whether or not, for each station, monthly samples of relative wind speed data for which mean values, v ˉ r fall within a same interval with the width equal to 0.05, have also the same variance, originate from the same population and thus possess the same statistical distribution. Thirdly, to build such a distribution referred to as a monthly reference frequency distribution of relative wind speed data, for any of the identified mean relative wind speed ( v ˉ r ) intervals of interest and for each station. All the three objectives have been attained.

References

[1]  Teetz, H.W., Harms, T.M. and von Backström, T.W. (2003) Assessment of the Wind Power Potential at SANAE IV Base, Antarctica: A Technical and Economic Feasibility Study. Renewable Energy, 28, 2037-2061.
https://doi.org/10.1016/s0960-1481(03)00076-4
[2]  Velázquez, M.T., Rodríguez, J.H., Carmen, M.V.D., Murrieta, F.E.F. and Eslava, G.T. (2016) Application of the Weibull Distribution to Estimate the Volume of Water Pumping by a Windmill. Journal of Power and Energy Engineering, 4, 36-51.
https://doi.org/10.4236/jpee.2016.49004
[3]  Kazet, M.Y., Mouangue, R., Kuitche, A. and Ndjaka, J.M. (2016) Wind Energy Resource Assessment in Ngaoundere Locality. Energy Procedia, 93, 74-81.
https://doi.org/10.1016/j.egypro.2016.07.152
[4]  Boudia, S.M., Berrached, S. and Bouri, S. (2016) On the Use of Wind Energy at Tlemcen, North-Western Region of Algeria. Energy Procedia, 93, 141-145.
https://doi.org/10.1016/j.egypro.2016.07.162
[5]  Messaoudi, D., Settou, N., Negrou, B., Rahmouni, S., Settou, B. and Mayou, I. (2019) Site Selection Methodology for the Wind-Powered Hydrogen Refueling Station Based on AHP-GIS in Adrar, Algeria. Energy Procedia, 162, 67-76.
https://doi.org/10.1016/j.egypro.2019.04.008
[6]  Ummhani, I.A. and Manahil, E.E.M. (2024) Site Selection for Wind Energy Farm Using GIS-Based Multicriteria Decision Analysis (MCDA) in Qassim Area. Migration Letters, 21, 57-68.
[7]  Qiu, X., Li, Y., Li, J., Wang, B. and Liu, Y. (2024) WindFormer: Learning Generic Representations for Short-Term Wind Speed Prediction. Applied Sciences, 14, Article 6741.
https://doi.org/10.3390/app14156741
[8]  Xu, D., Xue, F., Wu, Y., Li, Y., Liu, W., Xu, C., et al. (2024) Analysis of Wind Resource Characteristics in the Ulanqab Wind Power Base (Wind Farm): Mesoscale Modeling Approach. Energies, 17, Article 3540.
https://doi.org/10.3390/en17143540
[9]  Bashahu, M., Nsabimana, P., Barakamfitiye, J. and Niyukuri, F. (2022) Assessment of the Wind Energy Potential of Two Burundian Sites. Energy and Power Engineering, 14, 181-200.
https://doi.org/10.4236/epe.2022.145010
[10]  Baseer, M.A., Meyer, J.P., Rehman, S. and Alam, M.M. (2017) Wind Power Characteristics of Seven Data Collection Sites in Jubail, Saudi Arabia Using Weibull Parameters. Renewable Energy, 102, 35-49.
https://doi.org/10.1016/j.renene.2016.10.040
[11]  Azad, K., Rasul, M., Halder, P. and Sutariya, J. (2019) Assessment of Wind Energy Prospect by Weibull Distribution for Prospective Wind Sites in Australia. Energy Procedia, 160, 348-355.
https://doi.org/10.1016/j.egypro.2019.02.167
[12]  Galarza, J. (2021) Assessment of Wind Energy Potential and the Application for Micro-Turbines. International Journal of Electrical Engineering and Technology, 12, 20-31.
https://doi.org/10.34218/ijeet.12.9.2021.003
[13]  Idriss, A.I., Ahmed, R.A., Atteyeh, H.A., Omar, A.I. and Akinci, T.C. (2024) Techno-economic Assessment and Wind Energy Potential of Nagad in Djibouti. International Journal of Applied Power Engineering (IJAPE), 13, 91-101.
https://doi.org/10.11591/ijape.v13.i1.pp91-101
[14]  Mathew, S., Pandey, K.P. and Kumar.V, A. (2002) Analysis of Wind Regimes for Energy Estimation. Renewable Energy, 25, 381-399.
https://doi.org/10.1016/s0960-1481(01)00063-5
[15]  Karsli, V.M. and Geçit, C. (2003) An Investigation on Wind Power Potential of Nurdaǧı-Gaziantep, Turkey. Renewable Energy, 28, 823-830.
https://doi.org/10.1016/s0960-1481(02)00059-9
[16]  Parajuli, A. (2016) A Statistical Analysis of Wind Speed and Power Density Based on Weibull and Rayleigh Models of Jumla, Nepal. Energy and Power Engineering, 08, 271-282.
https://doi.org/10.4236/epe.2016.87026
[17]  Nielsen, M.A. (2011) Parameter Estimation for the Two-Parameter Weibull Distribution. Ph.D. Thesis, Brigham Young University.
[18]  Ayik, A., Ijumba, N., Kabiri, C. and Goffin, P. (2021) Preliminary Wind Resource Assessment in South Sudan Using Reanalysis Data and Statistical Methods. Renewable and Sustainable Energy Reviews, 138, Article ID: 110621.
https://doi.org/10.1016/j.rser.2020.110621
[19]  Gatoto, P., Lollchund, M.R. and Dalson, G.A. (2021) Wind Energy Potential Assessment of Some Sites in Burundi Using Statistical Modeling. Proceedings of 2021 IEEE PES/IAS Power Africa Virtual Conference, Nairobi, 23-27 August 2021, 219-223.
[20]  Alzubaidi, M., Hasan, K.N. and Meegahapola, L. (2020) Identification of Suitable Probability Density Function for Wind Speed Profiles in Power System Studies. Proceedings of the 2020 Australian Universities Power Engineering Conference (AUPEC), Hobart, 29 November-2 December 2020, 1-6.
[21]  Wais, P. (2017) Two and Three-Parameter Weibull Distribution in Available Wind Power Analysis. Renewable Energy, 103, 15-29.
https://doi.org/10.1016/j.renene.2016.10.041
[22]  Kidmo, D.K., Deli, K., Raidandi, D. and Yamigno, S.D. (2016) Wind Energy for Electricity Generation in the Far North Region of Cameroon. Energy Procedia, 93, 66-73.
https://doi.org/10.1016/j.egypro.2016.07.151
[23]  Plan National de Développement (PND Burundi 2018-2027) (2018) Annexe2: 73-87.
[24]  Bashahu, M. and Buseke, M. (2016) Statistical Analysis of Hourly Wind Speed Data from Some Burundian Stations Using Beta Probability Density Functions. Modern Environmental Science and Engineering, 2, 740-746.
https://doi.org/10.15341/mese(2333-2581)/11.02.2016/005
[25]  Placide, G. and Lollchund, M.R. (2024) Wind Farm Site Selection Using GIS-Based Mathematical Modeling and Fuzzy Logic Tools: A Case Study of Burundi. Frontiers in Energy Research, 12, Article 1353388.
https://doi.org/10.3389/fenrg.2024.1353388
[26]  Bashahu, M., Nsabimana, J.C. and Manirakiza, F. (2006) β Probability Density Functions and Reference Distributions of Relative Sunshine Duration Data from Burundian Stations. ISESCO Science and Technology Vision, 2, 30-34.
[27]  Bashahu, M. and Manirakiza, E. (2013) Monthly Reference Frequency Distributions of Daily Clearness Index Data from Burundian Stations. International Review of Physics (IREPHY), 7, 352-357.
[28]  Ilinca, A., McCarthy, E., Chaumel, J. and Rétiveau, J. (2003) Wind Potential Assessment of Quebec Province. Renewable Energy, 28, 1881-1897.
https://doi.org/10.1016/s0960-1481(03)00072-7
[29]  Naskar, S. and Das, P. (2018) Application of Different Statistical Tests in Educational Research: An Overview. Journal of Emerging Technologies and Innovative Research (JETIR), 5, 129-137.
[30]  Kumar, N.K. (2024) F-Test and Analysis of Variance (ANOVA) in Economics. Mikailalsys Journal of Mathematics and Statistics, 2, 102-113.
https://doi.org/10.58578/mjms.v2i3.3449
[31]  Kebalepile, M.M. and Chakane, P.M. (2022) Commonly Used Statistical Tests and their Application. Southern African Journal of Anaesthesia and Analgesia, 28, 580-584.
[32]  Barbaro, S., Cannata, G. and Coppolino, S. (1983) Monthly Reference Distribution of Daily Relative Sunshine Values. Solar Energy, 31, 63-67.
https://doi.org/10.1016/0038-092x(83)90034-8
[33]  Sarty, G. (2020) F-Distribution Table. University of Saskatchewan.
https://www.saskoer.ca_sites_2020/05
[34]  Dougherty, C. (2002) Introduction to Econometrics. 2nd Edition, Oxford University Press.
[35]  Marshall, E., Boggis, E., Patel, C., Emmett, M., Owen, A., Russell, J. and Fieller, N. The Statistics Tutor’s Quick Guide to Commonly Used Statistical Tests. Statstutor Community Project, University of Sheffield.
http://www.statstutor.ac.uk

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